In games with an extremely large action set, one popular approach to reduce the complexity necessary to solve the game is the double oracle method [1]}. With the double oracle algorithm, additional actions are added one by one as needed into the regret minimization procedure instead of considering the full set of actions all at once at the start of the game.
We introduce two changes to the double oracle algorithm that we demonstrate significantly improve convergence speed. First, we use greedy weights in place of vanilla regret matching, as has typically been done throughout this paper. Secondly, we seed the first iteration of the next round of the double oracle algorithm with the approximate equilibria discovered in the latest round of regret minimization.
